Bibliometric analysis for publications related to measurement error in geometric morphometrics
Robert Z. Selden, Jr.
24 May, 2021
Bibliometrics
The dataset used in this analysis was harvested from Scopus, includes all works recovered using the query “measurement error” AND “geometric morphometric,” and was analysed using the bibliometrix package (Aria and Cuccurullo 2017).
# install bibliometrix and load data
# devtools::install_github("massimoaria/bibliometrix")
# load
library(here)## here() starts at C:/Users/seldenjrz/Desktop/meas.errorlibrary(bibliometrix)## To cite bibliometrix in publications, please use:
##
## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis,
## Journal of Informetrics, 11(4), pp 959-975, Elsevier.
##
##
## https://www.bibliometrix.org
##
##
## For information and bug reports:
## - Send an email to info@bibliometrix.org
## - Write a post on https://github.com/massimoaria/bibliometrix/issues
##
## Help us to keep Bibliometrix free to download and use by contributing with a small donation to support our research team (https://bibliometrix.org/donate.html)
##
##
## To start with the shiny web-interface, please digit:
## biblioshiny()library(reshape2)
library(ggplot2)
# data frame
df <- convert2df(file = "scopus.bib",
dbsource = "scopus",
format = "bibtex")##
## Converting your scopus collection into a bibliographic dataframe
##
## Done!
##
##
## Generating affiliation field tag AU_UN from C1: Done!Descriptive analysis
results <- biblioAnalysis(df,
sep = ";")
options(width = 100)
s <- summary(object = results,
k = 20,
pause = FALSE)##
##
## MAIN INFORMATION ABOUT DATA
##
## Timespan 2007 : 2021
## Sources (Journals, Books, etc) 29
## Documents 47
## Average years from publication 4.98
## Average citations per documents 23
## Average citations per year per doc 3.015
## References 2463
##
## DOCUMENT TYPES
## article 45
## review 2
##
## DOCUMENT CONTENTS
## Keywords Plus (ID) 407
## Author's Keywords (DE) 159
##
## AUTHORS
## Authors 177
## Author Appearances 195
## Authors of single-authored documents 5
## Authors of multi-authored documents 172
##
## AUTHORS COLLABORATION
## Single-authored documents 5
## Documents per Author 0.266
## Authors per Document 3.77
## Co-Authors per Documents 4.15
## Collaboration Index 4.1
##
##
## Annual Scientific Production
##
## Year Articles
## 2007 1
## 2010 3
## 2011 5
## 2012 2
## 2013 1
## 2014 3
## 2015 2
## 2016 5
## 2017 4
## 2018 6
## 2019 6
## 2020 7
## 2021 2
##
## Annual Percentage Growth Rate 5.075664
##
##
## Most Productive Authors
##
## Authors Articles Authors Articles Fractionalized
## 1 CARDINI A 5 CARDINI A 2.283
## 2 BUSH MA 4 FRUCIANO C 1.292
## 3 BUSH PJ 4 BUSH MA 1.167
## 4 FRUCIANO C 3 BUSH PJ 1.167
## 5 SHEETS HD 3 BAKKES DK 1.000
## 6 ADALIAN P 2 ITO T 1.000
## 7 EVIN A 2 MENNDEZ LP 1.000
## 8 FUSCO G 2 SHEETS HD 0.833
## 9 SCHLAGER S 2 CORNY J 0.500
## 10 ABIZANDA J 1 DOUGLAS TS 0.500
## 11 AIELLO LC 1 DTROIT F 0.500
## 12 ALLEN R 1 LOOPE KJ 0.500
## 13 AMEEN C 1 MUTSVANGWA TEM 0.500
## 14 ARCHER W 1 PERRARD A 0.500
## 15 ARREOLA-RAMOS R 1 ROBINSON C 0.500
## 16 AVILA LJ 1 TERHUNE CE 0.500
## 17 AVILA VALLE Z 1 VISCOSI V 0.500
## 18 BAKKES DK 1 ADALIAN P 0.450
## 19 BEERLINK A 1 EVIN A 0.444
## 20 BIFFAR R 1 FUSCO G 0.400
##
##
## Top manuscripts per citations
##
## Paper DOI TC TCperYear NTC
## 1 VISCOSI V, 2011, PLOS ONE 10.1371/journal.pone.0025630 221 20.091 2.939
## 2 VON CRAMON-TAUBADEL N, 2007, AM J PHYS ANTHROPOL 10.1002/ajpa.20616 151 10.067 1.000
## 3 NOBACK ML, 2011, AM J PHYS ANTHROPOL 10.1002/ajpa.21523 114 10.364 1.516
## 4 FRUCIANO C, 2016, DEV GENES EVOL 10.1007/s00427-016-0537-4 82 13.667 2.611
## 5 CARDINI A, 2014, HYSTRIX 10.4404/hystrix-25.2-10993 74 9.250 2.440
## 6 EVIN A, 2016, J ARCHAEOL SCI REP 10.1016/j.jasrep.2016.06.028 50 8.333 1.592
## 7 FRUCIANO C, 2017, ECOLOGY AND EVOLUTION 10.1002/ece3.3256 31 6.200 1.699
## 8 MUIR AM, 2012, TRANS AM FISH SOC 10.1080/00028487.2012.685823 31 3.100 1.590
## 9 DE GROOTE I, 2010, AM J PHYS ANTHROPOL 10.1002/ajpa.21225 31 2.583 1.348
## 10 ROBINSON C, 2017, AM J PHYS ANTHROPOL 10.1002/ajpa.23257 28 5.600 1.534
## 11 DOUGLAS TS, 2010, AM J MED GENET PART A 10.1002/ajmg.a.33276 27 2.250 1.174
## 12 SHEETS HD, 2011, J FORENSIC SCI 10.1111/j.1556-4029.2011.01731.x 21 1.909 0.279
## 13 FUESSINGER MA, 2018, INT J COMPUT ASSISTED RADIOL SURG 10.1007/s11548-017-1674-6 18 4.500 2.000
## 14 BUSH MA, 2011, FORENSIC SCI INT 10.1016/j.forsciint.2011.03.028 18 1.636 0.239
## 15 ARCHER W, 2018, ARCHAEOL ANTHROPOL SCI 10.1007/s12520-017-0517-2 17 4.250 1.889
## 16 DAVID SHEETS H, 2013, J FORENSIC SCI 10.1111/j.1556-4029.2012.02293.x 16 1.778 1.000
## 17 COSMAN MN, 2016, J ANAT 10.1111/joa.12459 12 2.000 0.382
## 18 CORNY J, 2014, AM J PHYS ANTHROPOL 10.1002/ajpa.22428 12 1.500 0.396
## 19 DABOUL A, 2018, PLOS ONE 10.1371/journal.pone.0197675 11 2.750 1.222
## 20 PALMER M, 2010, MAR ECOL PROG SER 10.3354/meps08347 11 0.917 0.478
##
##
## Corresponding Author's Countries
##
## Country Articles Freq SCP MCP MCP_Ratio
## 1 USA 9 0.1957 7 2 0.222
## 2 GERMANY 7 0.1522 2 5 0.714
## 3 ITALY 7 0.1522 1 6 0.857
## 4 FRANCE 6 0.1304 3 3 0.500
## 5 SOUTH AFRICA 3 0.0652 2 1 0.333
## 6 SPAIN 3 0.0652 3 0 0.000
## 7 UNITED KINGDOM 3 0.0652 0 3 1.000
## 8 AUSTRALIA 2 0.0435 2 0 0.000
## 9 CANADA 2 0.0435 1 1 0.500
## 10 ARGENTINA 1 0.0217 1 0 0.000
## 11 JAPAN 1 0.0217 1 0 0.000
## 12 MEXICO 1 0.0217 1 0 0.000
## 13 NORWAY 1 0.0217 1 0 0.000
##
##
## SCP: Single Country Publications
##
## MCP: Multiple Country Publications
##
##
## Total Citations per Country
##
## Country Total Citations Average Article Citations
## 1 ITALY 400 57.14
## 2 UNITED KINGDOM 190 63.33
## 3 GERMANY 176 25.14
## 4 USA 133 14.78
## 5 FRANCE 69 11.50
## 6 AUSTRALIA 37 18.50
## 7 SOUTH AFRICA 35 11.67
## 8 SPAIN 16 5.33
## 9 CANADA 14 7.00
## 10 NORWAY 5 5.00
## 11 ARGENTINA 2 2.00
## 12 JAPAN 1 1.00
## 13 MEXICO 0 0.00
##
##
## Most Relevant Sources
##
## Sources Articles
## 1 AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 7
## 2 PLOS ONE 4
## 3 FORENSIC SCIENCE INTERNATIONAL 3
## 4 JOURNAL OF ANATOMY 3
## 5 JOURNAL OF FORENSIC SCIENCES 3
## 6 ARCHAEOLOGICAL AND ANTHROPOLOGICAL SCIENCES 2
## 7 DEVELOPMENT GENES AND EVOLUTION 2
## 8 ECOLOGY AND EVOLUTION 2
## 9 AMERICAN JOURNAL OF MEDICAL GENETICS PART A 1
## 10 BIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY 1
## 11 BIOLOGY METHODS AND PROTOCOLS 1
## 12 EVOLUTIONARY BIOLOGY 1
## 13 FORENSIC SCIENCE INTERNATIONAL: REPORTS 1
## 14 HYSTRIX 1
## 15 INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 1
## 16 ITALIAN JOURNAL OF ZOOLOGY 1
## 17 JOURNAL OF APICULTURAL RESEARCH 1
## 18 JOURNAL OF ARCHAEOLOGICAL SCIENCE: REPORTS 1
## 19 JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY 1
## 20 JOURNAL OF FISH BIOLOGY 1
##
##
## Most Relevant Keywords
##
## Author Keywords (DE) Articles Keywords-Plus (ID) Articles
## 1 GEOMETRIC MORPHOMETRICS 25 ARTICLE 24
## 2 MEASUREMENT ERROR 13 MALE 24
## 3 FORENSIC SCIENCE 6 FEMALE 23
## 4 LANDMARKS 5 HUMAN 23
## 5 BITEMARKS 4 MEASUREMENT ERROR 22
## 6 FORENSIC ODONTOLOGY 4 ADULT 18
## 7 FLUCTUATING ASYMMETRY 3 HUMANS 17
## 8 GEOMETRIC MORPHOMETRIC ANALYSIS 3 ANATOMY AND HISTOLOGY 15
## 9 MORPHOMETRICS 3 MORPHOMETRY 14
## 10 PHOTOGRAMMETRY 3 PRINCIPAL COMPONENT ANALYSIS 14
## 11 SHAPE 3 MORPHOMETRICS 13
## 12 ALLOMETRY 2 ANTHROPOMETRY 12
## 13 APIS MELLIFERA 2 GEOMETRY 12
## 14 BITEMARK RESEARCH 2 IMAGING 12
## 15 DENTAL UNIQUENESS 2 THREE-DIMENSIONAL 12
## 16 PHYLOGENETIC SIGNAL 2 IMAGE PROCESSING 11
## 17 PROCRUSTES 2 NONHUMAN 11
## 18 SHAPE ANALYSIS 2 PRIORITY JOURNAL 11
## 19 -CT IMAGING 1 ANIMAL 10
## 20 2D 1 ANIMALS 10# plot attributes
plot(x = results,
k = 20,
pause = FALSE)
Attributes of the local network
# calculate citations in local network
CR <- localCitations(df, sep = ";")
# top 20 cited authors in local network
CR$Authors[1:20,]## Author LocalCitations
## 28 CORNELIUS CP 14
## 48 ELLIS III 14
## 54 FUESSINGER MA 14
## 58 GASS M 14
## 105 METZGER MC 14
## 126 PROBST F 14
## 143 SCHLAGER S 14
## 146 SCHWARZ S 14
## 147 SEMPER-HOGG W 14
## 7 ARREOLA-RAMOS R 13
## 8 AVILA LJ 13
## 79 HUESA EGD 13
## 109 MORANDO M 13
## 137 SANCHEZ KI 13
## 164 VILLAGRA A 13
## 169 VRDOLJAK J 13
## 34 DARWIN BC 10
## 42 DEVINE J 10
## 67 HALLGRIMSSON B 10
## 73 HENKELMAN RM 10# top 20 cited papers in local network
CR$Papers[1:20,]## Paper DOI Year LCS GCS
## 28 FRUCIANO C, 2016, DEV GENES EVOL 10.1007/s00427-016-0537-4 2016 14 82
## 47 VON CRAMON-TAUBADEL N, 2007, AM J PHYS ANTHROPOL 10.1002/ajpa.20616 2007 13 151
## 35 CARDINI A, 2014, HYSTRIX 10.4404/hystrix-25.2-10993 2014 10 74
## 23 FRUCIANO C, 2017, ECOLOGY AND EVOLUTION 10.1002/ece3.3256 2017 9 31
## 24 ROBINSON C, 2017, AM J PHYS ANTHROPOL 10.1002/ajpa.23257 2017 7 28
## 19 DABOUL A, 2018, PLOS ONE 10.1371/journal.pone.0197675 2018 5 11
## 39 VISCOSI V, 2011, PLOS ONE 10.1371/journal.pone.0025630 2011 5 221
## 26 EVIN A, 2016, J ARCHAEOL SCI REP 10.1016/j.jasrep.2016.06.028 2016 4 50
## 8 FRUCIANO C, 2020, ZOOL J LINN SOC 10.1093/zoolinnean/zlz069 2020 3 5
## 40 BUSH MA, 2011, FORENSIC SCI INT 10.1016/j.forsciint.2011.03.028 2011 2 18
## 7 FOX NS, 2020, ECOLOGY AND EVOLUTION 10.1002/ece3.6063 2020 1 6
## 10 ITO T, 2019, METHODS ECOL EVOL 10.1111/2041-210X.13274 2019 1 1
## 12 ENGELKES K, 2019, J ANAT 10.1111/joa.12999 2019 1 7
## 25 MENNDEZ LP, 2017, J FORENSIC SCI 10.1111/1556-4029.13301 2017 1 9
## 43 SHEETS HD, 2011, J FORENSIC SCI 10.1111/j.1556-4029.2011.01731.x 2011 1 21
## 1 VRDOLJAK J, 2021, BIOL J LINN SOC 10.1093/BIOLINNEAN/BLAA079 2021 0 2
## 2 EVIN A, 2021, BIOL METHODS PROTOC 10.1093/biomethods/bpaa023 2021 0 0
## 3 JASSO-CULLAR J, 2020, FOREN SCI INTERNAT REP 10.1016/j.fsir.2020.100161 2020 0 0
## 4 COURTENAY LA, 2020, PLOS ONE 10.1371/journal.pone.0240328 2020 0 3
## 5 WASILJEW BD, 2020, J FISH BIOL 10.1111/jfb.14410 2020 0 0# top authors' productivity over time
topAU <- authorProdOverTime(df,
k = 20,
graph = TRUE)
# number of documents published annually by top sources
topSO <- sourceGrowth(df,
top = 8,
cdf = TRUE)
topSO## Year AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY PLOS ONE FORENSIC SCIENCE INTERNATIONAL JOURNAL OF ANATOMY
## 2007 2007 1 0 0 0
## 2008 2008 1 0 0 0
## 2009 2009 1 0 0 0
## 2010 2010 2 0 0 0
## 2011 2011 4 1 1 0
## 2012 2012 4 1 2 0
## 2013 2013 4 1 2 0
## 2014 2014 5 1 2 0
## 2015 2015 5 2 2 0
## 2016 2016 5 2 2 1
## 2017 2017 6 2 2 1
## 2018 2018 7 3 2 1
## 2019 2019 7 3 2 3
## 2020 2020 7 4 3 3
## 2021 2021 7 4 3 3
## JOURNAL OF FORENSIC SCIENCES ARCHAEOLOGICAL AND ANTHROPOLOGICAL SCIENCES DEVELOPMENT GENES AND EVOLUTION
## 2007 0 0 0
## 2008 0 0 0
## 2009 0 0 0
## 2010 0 0 0
## 2011 1 0 0
## 2012 1 0 0
## 2013 2 0 0
## 2014 2 0 0
## 2015 2 0 0
## 2016 2 0 2
## 2017 3 0 2
## 2018 3 1 2
## 2019 3 1 2
## 2020 3 2 2
## 2021 3 2 2
## ECOLOGY AND EVOLUTION
## 2007 0
## 2008 0
## 2009 0
## 2010 0
## 2011 0
## 2012 0
## 2013 0
## 2014 0
## 2015 0
## 2016 0
## 2017 1
## 2018 1
## 2019 1
## 2020 2
## 2021 2# plot results
tso = melt(topSO, id = 'Year')
ggplot(tso, aes(Year, value, group = variable, color = variable)) +
geom_line()
Most cited
Most cited articles
# most cited references in global network
mcr <- citations(df,
field = "article",
sep = ";")
cbind(mcr$Cited[1:20])## [,1]
## KLINGENBERG, C.P., BARLUENGA, M., MEYER, A., SHAPE ANALYSIS OF SYMMETRIC STRUCTURES: QUANTIFYING VARIATION AMONG INDIVIDUALS AND ASYMMETRY (2002) EVOLUTION, 56, PP. 1909-1920 6
## KLINGENBERG, C.P., MCINTYRE, G.S., GEOMETRIC MORPHOMETRICS OF DEVELOPMENTAL INSTABILITY: ANALYZING PATTERNS OF FLUCTUATING ASYMMETRY WITH PROCRUSTES METHODS (1998) EVOLUTION, 52, PP. 1363-1375 5
## KLINGENBERG, C.P., MORPHOJ: AN INTEGRATED SOFTWARE PACKAGE FOR GEOMETRIC MORPHOMETRICS (2011) MOLECULAR ECOLOGY RESOURCES, 11, PP. 353-357 4
## LOCKWOOD, C.A., LYNCH, J.M., KIMBEL, W.H., QUANTIFYING TEMPORAL BONE MORPHOLOGY OF GREAT APES AND HUMANS: AN APPROACH USING GEOMETRIC MORPHOMETRICS (2002) J ANAT, 201, PP. 447-464 4
## ROHLF, F.J., MARCUS, L.F., A REVOLUTION IN MORPHOMETRICS (1993) TRENDS ECOL EVOL, 8, PP. 129-132 4
## ADAMS, D.C., ROHLF, F.J., SLICE, D.E., GEOMETRIC MORPHOMETRICS: TEN YEARS OF PROGRESS FOLLOWING THE REVOLUTION (2004) ITAL J ZOOL, 71, PP. 5-16 3
## BOOKSTEIN, F.L., (1991) MORPHOMETRIC TOOLS FOR LANDMARK DATA: GEOMETRY AND BIOLOGY, , CAMBRIDGE UNIVERSITY PRESS, CAMBRIDGE 3
## BOOKSTEIN, F.L., LANDMARK METHODS FOR FORMS WITHOUT LANDMARKS: MORPHOMETRICS OF GROUP DIFFERENCES IN OUTLINE SHAPE (1997) MEDICAL IMAGE ANALYSIS, 1, PP. 225-243 3
## BUSER, T.J., SIDLAUSKAS, B.L., SUMMERS, A.P., 2D OR NOT 2D? TESTING THE UTILITY OF 2D VS. 3D LANDMARK DATA IN GEOMETRIC MORPHOMETRICS OF THE SCULPIN SUBFAMILY OLIGOCOTTINAE (PISCES 3
## CARDINI, A., ELTON, S., SAMPLE SIZE AND SAMPLING ERROR IN GEOMETRIC MORPHOMETRIC STUDIES OF SIZE AND SHAPE (2007) ZOOMORPHOLOGY, 126, PP. 121-134 3
## FRUCIANO, C., MEASUREMENT ERROR IN GEOMETRIC MORPHOMETRICS (2016) DEV GENES EVOL, 226, PP. 139-158 3
## FRUCIANO, C., MEASUREMENT ERROR IN GEOMETRIC MORPHOMETRICS (2016) DEVELOPMENT GENES AND EVOLUTION, 226, PP. 139-158 3
## KLINGENBERG, C.P., BARLUENGA, M., MEYER, A., SHAPE ANALYSIS OF SYMMETRIC STRUCTURES: QUANTIFYING VARIATION AMONG INDIVIDUALS AND ASYMMETRY (2002) EVOLUTION, 56, PP. 1909-1920. , PID: 12449478 3
## KLINGENBERG, C.P., MONTEIRO, L.R., DISTANCES AND DIRECTIONS IN MULTIDIMENSIONAL SHAPE SPACES: IMPLICATIONS FOR MORPHOMETRIC APPLICATIONS (2005) SYST BIOL, 54, PP. 678-688 3
## MITTEROECKER, P., GUNZ, P., ADVANCES IN GEOMETRIC MORPHOMETRICS (2009) EVOL BIOL, 36, PP. 235-247 3
## ROHLF, F.J., THE TPS SERIES OF SOFTWARE (2015) HYSTRIX, THE ITALIAN JOURNAL OF MAMMALOGY, 26, PP. 9-12 3
## SLICE, D.E., GEOMETRIC MORPHOMETRICS (2007) ANNU REV ANTHROPOL, 36, PP. 261-281 3
## VON CRAMON-TAUBADEL, N., FRAZIER, B.C., LAHR, M.M., THE PROBLEM OF ASSESSING LANDMARK ERROR IN GEOMETRIC MORPHOMETRICS: THEORY, METHODS, AND MODIFICATIONS (2007) AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY, 134, PP. 24-35 3
## (2008) R: A LANGUAGE AND ENVIRONMENT FOR STATISTICAL COMPUTING, , VIENNA, R FOUNDATION FOR STATISTICAL COMPUTING 2
## ADAMS, D.C., COLLYER, M.L., PERMUTATION TESTS FOR PHYLOGENETIC COMPARATIVE ANALYSES OF HIGH-DIMENSIONAL SHAPE DATA: WHAT YOU SHUFFLE MATTERS (2015) EVOLUTION, 69 (3), PP. 823-829 2Intellectual structure
Author co-citation
Co-citation analysis is the most commonly used bibliometric analysis method (Ding, Chowdhury, and Foo 2001), and is defined as two publications that are cited together in one article (Small 1973).
# extract author names from reference items
df <- metaTagExtraction(df,
Field = "CR_AU")
# author co-citation network
auth.co.mat <- biblioNetwork(df,
analysis = "co-citation",
network = "authors",
sep = ";")
# network plot
auth.co.net = networkPlot(auth.co.mat,
n = 50,
Title = "Author Co-Citation Network",
type = "auto",
size = 10,
size.cex = T,
remove.multiple = FALSE,
labelsize = 0.5,
edgesize = 8,
edges.min = 3,
remove.isolates = TRUE)
# descriptive analysis of author co-citation network
auth.co.netstat <- networkStat(auth.co.mat)
summary(auth.co.netstat, k = 15)##
##
## Main statistics about the network
##
## Size 1283
## Density 0.063
## Transitivity 0.449
## Diameter 3
## Degree Centralization 0.689
## Average path length 1.98
## Author coupling
Coupling is a similarity measure that uses citation analysis to illustrate a similarity relationship between documents. Author coupling occurs when two authors reference a common third author in their bibliographies.
# author coupling network
auth.coup.mat <- biblioNetwork(df,
analysis = "coupling",
network = "authors",
sep = ";")
# network plot
auth.coup.net = networkPlot(auth.coup.mat,
n = 50,
Title = "Author Coupling Network",
type = "mds",
size = 10,
size.cex = T,
remove.multiple = FALSE,
labelsize = 0.5,
edgesize = 5,
edges.min = 8,
remove.isolates = TRUE)
# descriptive analysis of author coupling network
auth.coup.netstat <- networkStat(auth.coup.mat)
summary(auth.coup.netstat, k = 15)##
##
## Main statistics about the network
##
## Size 177
## Density 0.48
## Transitivity 0.696
## Diameter 3
## Degree Centralization 0.463
## Average path length 1.543
## Historiograph direct citation linkages
The historiographic map is a chronological network map of the most relevant direct citations resulting from this bibliographic collection.
# historical citation network
options(width = 100)
histResults <- histNetwork(df,
min.citations = 10,
sep = ";")##
## SCOPUS DB: Searching local citations (LCS) by document titles (TI) and DOIs...
##
## Found 10 documents with no empty Local Citations (LCS)# plot historical co-citation network
hnet <- histPlot(histResults,
n = 100,
size = 6,
labelsize = 2)
##
## Legend
##
## Label DOI Year LCS GCS
## 1 DABOUL A, 2018, PLOS ONE 10.1371/journal.pone.0197675 2018 5 11
## 2 FRUCIANO C, 2017, ECOLOGY AND EVOLUTION 10.1002/ece3.3256 2017 9 31
## 3 ROBINSON C, 2017, AM J PHYS ANTHROPOL 10.1002/ajpa.23257 2017 7 28
## 4 EVIN A, 2016, J ARCHAEOL SCI REP 10.1016/j.jasrep.2016.06.028 2016 4 50
## 5 FRUCIANO C, 2016, DEV GENES EVOL 10.1007/s00427-016-0537-4 2016 14 82
## 6 CARDINI A, 2014, HYSTRIX 10.4404/hystrix-25.2-10993 2014 10 74
## 7 VISCOSI V, 2011, PLOS ONE 10.1371/journal.pone.0025630 2011 5 221
## 8 BUSH MA, 2011, FORENSIC SCI INT 10.1016/j.forsciint.2011.03.028 2011 2 18
## 9 SHEETS HD, 2011, J FORENSIC SCI 10.1111/j.1556-4029.2011.01731.x 2011 1 21
## 10 VON CRAMON-TAUBADEL N, 2007, AM J PHYS ANTHROPOL 10.1002/ajpa.20616 2007 13 151
## 11 EVIN A, 2021, BIOL METHODS PROTOC 10.1093/biomethods/bpaa023 2021 0 0
## 12 COURTENAY LA, 2020, PLOS ONE 10.1371/journal.pone.0240328 2020 0 3
## 13 FRUCIANO C, 2020, ZOOL J LINN SOC 10.1093/zoolinnean/zlz069 2020 0 5
## 14 ENGELKES K, 2019, J ANAT 10.1111/joa.12999 2019 0 7
## 15 GALIMBERTI F, 2019, J ZOOL SYST EVOL RES 10.1111/jzs.12276 2019 0 2
## 16 VRDOLJAK J, 2021, BIOL J LINN SOC 10.1093/BIOLINNEAN/BLAA079 2021 0 2
## 17 WASILJEW BD, 2020, J FISH BIOL 10.1111/jfb.14410 2020 0 0
## 18 FOX NS, 2020, ECOLOGY AND EVOLUTION 10.1002/ece3.6063 2020 0 6
## 19 ITO T, 2019, METHODS ECOL EVOL 10.1111/2041-210X.13274 2019 0 1
## 20 GIACOMINI G, 2019, EVOL BIOL 10.1007/s11692-019-09478-6 2019 0 8
## 21 MOPIN C, 2018, AM J PHYS ANTHROPOL 10.1002/ajpa.23613 2018 0 0
## 22 MACDONALD DA, 2020, ARCHAEOL ANTHROPOL SCI 10.1007/s12520-020-01111-4 2020 0 0
## 23 RIDEL AF, 2020, FORENSIC SCI INT 10.1016/j.forsciint.2019.110095 2020 0 3
## 24 BAKKES DK, 2017, ZOOMORPHOLOGY 10.1007/s00435-017-0357-8 2017 0 5
## 25 DAVID SHEETS H, 2013, J FORENSIC SCI 10.1111/j.1556-4029.2012.02293.x 2013 0 16
## 26 SHEETS HD, 2012, FORENSIC SCI INT 10.1016/j.forsciint.2012.08.044 2012 0 8
## 27 JASSO-CULLAR J, 2020, FOREN SCI INTERNAT REP 10.1016/j.fsir.2020.100161 2020 0 0
## 28 MENNDEZ LP, 2017, J FORENSIC SCI 10.1111/1556-4029.13301 2017 0 9
## 29 MUIR AM, 2012, TRANS AM FISH SOC 10.1080/00028487.2012.685823 2012 0 31ID and DE keyword associations
list <- keywordAssoc(df,
sep = ";",
n = 10)
list[[1]][1:10]## GEOMETRIC MORPHOMETRICS FORENSIC SCIENCE BITEMARKS
## 8 5 4
## FORENSIC ODONTOLOGY MEASUREMENT ERROR GEOMETRIC MORPHOMETRIC ANALYSIS
## 4 3 3
## FLUCTUATING ASYMMETRY MORPHOMETRICS BITEMARK RESEARCH
## 2 2 2
## DENTAL UNIQUENESS
## 2Yearly occurrences of top keywords/terms
Authors’ keywords
topKW = KeywordGrowth(df,
Tag = "DE",
sep = ";",
top = 10,
cdf = TRUE)
topKW## Year GEOMETRIC MORPHOMETRICS MEASUREMENT ERROR FORENSIC SCIENCE LANDMARKS BITEMARKS
## 1 2007 0 1 0 0 0
## 2 2008 0 1 0 0 0
## 3 2009 0 1 0 0 0
## 4 2010 2 1 0 0 0
## 5 2011 4 1 2 1 2
## 6 2012 4 1 3 1 3
## 7 2013 4 1 4 1 4
## 8 2014 6 2 4 2 4
## 9 2015 7 2 4 2 4
## 10 2016 11 3 4 2 4
## 11 2017 14 6 5 2 4
## 12 2018 14 6 5 3 4
## 13 2019 18 10 5 4 4
## 14 2020 23 11 6 5 4
## 15 2021 25 13 6 5 4
## FORENSIC ODONTOLOGY FLUCTUATING ASYMMETRY GEOMETRIC MORPHOMETRIC ANALYSIS MORPHOMETRICS
## 1 0 0 0 1
## 2 0 0 0 1
## 3 0 0 0 1
## 4 0 1 0 2
## 5 2 1 1 2
## 6 3 1 2 2
## 7 4 1 3 2
## 8 4 1 3 2
## 9 4 1 3 2
## 10 4 2 3 2
## 11 4 2 3 2
## 12 4 3 3 2
## 13 4 3 3 2
## 14 4 3 3 3
## 15 4 3 3 3
## PHOTOGRAMMETRY
## 1 0
## 2 0
## 3 0
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 2
## 11 3
## 12 3
## 13 3
## 14 3
## 15 3# plot results
key.plot = melt(topKW,
id ='Year')
ggplot(key.plot, aes(Year,
value,
group = variable,
color = variable)) +
geom_line()
Publisher’s keywords
topKW = KeywordGrowth(df,
Tag = "ID",
sep = ";",
top = 10,
cdf = TRUE)
topKW## Year ARTICLE MALE FEMALE HUMAN MEASUREMENT ERROR ADULT HUMANS ANATOMY AND HISTOLOGY MORPHOMETRY
## 1 2007 1 0 0 0 0 0 0 0 0
## 2 2008 1 0 0 0 0 0 0 0 0
## 3 2009 1 0 0 0 0 0 0 0 0
## 4 2010 2 1 1 2 1 0 2 1 1
## 5 2011 7 4 4 7 5 1 5 1 1
## 6 2012 8 4 4 9 6 1 6 1 1
## 7 2013 9 4 4 11 6 1 7 1 1
## 8 2014 10 4 4 12 7 1 8 1 2
## 9 2015 11 8 8 13 8 3 9 3 3
## 10 2016 14 12 12 14 12 5 10 5 3
## 11 2017 15 13 12 15 13 6 11 7 4
## 12 2018 19 20 19 18 17 13 14 9 8
## 13 2019 21 22 21 19 19 16 15 12 10
## 14 2020 24 24 23 23 22 18 17 15 13
## 15 2021 24 24 23 23 22 18 17 15 14
## PRINCIPAL COMPONENT ANALYSIS
## 1 0
## 2 0
## 3 0
## 4 2
## 5 6
## 6 6
## 7 8
## 8 8
## 9 8
## 10 9
## 11 11
## 12 11
## 13 11
## 14 14
## 15 14# plot results
key.plot = melt(topKW,
id ='Year')
ggplot(key.plot, aes(Year,
value,
group = variable,
color = variable)) +
geom_line()
Conceptual structure
Co-word analysis
The co-word analysis maps the conceptual structure of a research domain using the co-occurrence of author keywords in the bibliographic collection.
Authors’ keywords
# using authors keywords
cw <- conceptualStructure(df,
field = "DE",
method = "MDS",
minDegree = 2,
clust = "auto",
stemming = FALSE,
labelsize = 10,
documents = 50)
Publisher’s keywords
# using publishers keywords
cw <- conceptualStructure(df,
field = "ID",
method = "MDS",
minDegree = 2,
clust = "auto",
stemming = FALSE,
labelsize = 6,
documents = 50)
Thematic mapping
From (Cobo et al. 2011, 150–51):
- Themes in the upper-right quadrant are both well developed and important for the structuring ofa research field. They are known as the motor-themes of the specialty, given that they present strong centrality and high density. The placement of themesin this quadrantimplies that theyare related externally to concepts applicable to otherthemesthat are conceptually closely related.
- Themes in the upper-left quadrant have well developed internal ties but unimportant external ties and so are of only marginal importance for the field. These themes are very specialized and peripheral in character.
- Themes in the lower-left quadrant are both weakly developed and marginal. The themes ofthis quadrant have low density and low centrality, mainly representing either emerging or disappearing themes.
- Themes in the lower-right quadrant are important for a research field but are not developed. So, this quadrant groups transversal and general, basic themes.
Authors’ keywords
# keyword map
map1 = thematicMap(df,
field = "DE",
n = 1000,
minfreq = 3,
stemming = FALSE,
size = 0.8,
n.labels = 1,
repel = TRUE)
# plot map
plot(map1$map)
Publisher’s keywords
# keyword map
map1 = thematicMap(df,
field = "ID",
n = 1000,
minfreq = 3,
stemming = FALSE,
size = 0.8,
n.labels = 1,
repel = TRUE)
# plot map
plot(map1$map)
Colophon
This version of the analysis was generated on 2021-05-24 04:06:47 using the following computational environment and dependencies:
# what R packages and versions were used?
if ("devtools" %in% installed.packages()) devtools::session_info()## - Session info -----------------------------------------------------------------------------------
## setting value
## version R version 4.0.5 (2021-03-31)
## os Windows 10 x64
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United States.1252
## ctype English_United States.1252
## tz America/Chicago
## date 2021-05-24
##
## - Packages ---------------------------------------------------------------------------------------
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##
## [1] C:/Users/seldenjrz/Documents/R/win-library/4.0
## [2] C:/Program Files/R/R-4.0.5/libraryCurrent Git commit details are:
# where can I find this commit?
if ("git2r" %in% installed.packages() & git2r::in_repository(path = ".")) git2r::repository(here::here()) References cited
Aria, Massimo, and Corrado Cuccurullo. 2017. “Bibliometrix : An r-Tool for Comprehensive Science Mapping Analysis.” Journal of Informetrics 11 (4): 959–75. https://doi.org/10.1016/j.joi.2017.08.007.
Cobo, M. J., A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera. 2011. “An Approach for Detecting, Quantifying, and Visualizing the Evolution of a Research Field: A Practical Application to the Fuzzy Sets Theory Field.” Journal Article. Journal of Informetrics 5 (1): 146–66. https://doi.org/10.1016/j.joi.2010.10.002.
Ding, Ying, Gobinda G. Chowdhury, and Schubert Foo. 2001. “Bibliometric Cartography of Information Retrieval Research by Using Co-Word Analysis.” Information Processing & Management 37 (6): 817–42. https://doi.org/10.1016/s0306-4573(00)00051-0.
Small, Henry. 1973. “Co-Citation in the Scientific Literature: A New Measure of the Relationship Between Two Documents.” Journal of the American Society for Information Science 24 (4): 265–69. https://doi.org/10.1002/asi.4630240406.
Social structure
Author collaboration
Scientific collaborations are plotted where nodes are authors and links are co-authorships, illustrating collaborations between authors.
Edu collaboration
Scientific collaborations are plotted where nodes are institutions and links are co-authorships, illustrating collaborations between institutions.
Country collaboration
In this figure, scientific collaborations are plotted where nodes are countries and links are co-authorships, illustrating collaborations between countries